Nearly all data shown here is from the South Africa National Institue for Communicable Diseases (NICD), but it is accessed through different channels. Cases, deaths, and testing data are retrieved from Our World in Data on GitHub via Johns Hopkins and NICD. Hospitalization data and provincial data are retrieved from from the Data Science for Social Impact Research Group @ University of Pretoria, Coronavirus COVID-19 (2019-nCoV) Data Repository for South Africa. Available on: https://github.com/dsfsi/covid19za. Many thanks to all who have worked to collect this data and make it publicly accessible.

I display data since the beginning of 2021. Dashed lines indicate the date (Nov 25, 2021) when the Omicron variant was announced by NICD. My processing and analysis code can be found here.

Cases

The line chart below shows the weekly growth multiplier of seven-day average cases. Values over 1 indicate case growth, while values under 1 mean case decline. For example, a 2.0 growth multiplier would mean cases are twice as high as the week before (rising); 0.5 would mean that they are only half as high (falling). Dots show daily values compared to seven days earlier.

Deaths

Percentage of peak values This charts display the 7-day average for deaths (black) and cases (orange) over time, expressed as the percentage of the all-time high values reached in summer 2021. Deaths are lagged by 17 days, the observed gap between the peak of cases and the peak of deaths for South Africa as a whole during the summer of 2021 (Delta wave). It is designed to explore differences in disease severity over time.

Case fatality rate This chart displays the 7-day average for deaths (lagged 17 days) divided by the 7-day average for cases. The lag reflects the observed gap between the peak of cases and the peak of deaths during the summer of 2021 (Delta wave). The chart includes a loess smoothing.

Testing and Positivity

Positive rate reflects the 7-day average for new reported cases divided by the 7-day average for new reported tests. When data from JHU/Our World in Data lags reported data, I instead use figures from Data Science for Social Impact Research Group (DSFSI) @ University of Pretoria via GitHub that include data on cumulative tests from NICD press releases. Provincial weekly positive rates are also from NSFSI.

The chart below displays weekly positivity rates for South Africa and Gauteng province reported by NICD and catalogued by DSFSI. Past weeks may be updated as more test results are reported.

Hospitals

Data from Data Science for Social Impact Research Group (DSFSI) @ University of Pretoria via GitHub. Presented first for South Africa as a whole and then for Gauteng Province specifically. DSFSI catalogs hospitalization data reported by NICD’s daily DATCOV hospital surveillance reports.

Percentage of Peak Values Case and hospitalization metrics (seven-day averages) over time as percentage of peak values for South Africa. No lags are applied. The gray area chart shows the progression of cases over time, while the lines show hospitalization metrics.

Data Table (JHU)

var date total weekday new avg_7day
cases 2022-02-05 3622210 Saturday 6135 2940.0000
cases 2022-02-06 3623962 Sunday 1752 2872.2857
cases 2022-02-07 3625190 Monday 1228 2852.5714
cases 2022-02-08 3626014 Tuesday 824 2529.5714
cases 2022-02-09 3631642 Wednesday 5628 2690.4286
cases 2022-02-10 3634811 Thursday 3169 2676.5714
cases 2022-02-11 3637673 Friday 2862 3085.4286
cases 2022-02-12 3640162 Saturday 2489 2564.5714
deaths 2022-02-05 95817 Saturday 272 130.2857
deaths 2022-02-06 95835 Sunday 18 116.1429
deaths 2022-02-07 96021 Monday 186 132.5714
deaths 2022-02-08 96289 Tuesday 268 143.0000
deaths 2022-02-09 96502 Wednesday 213 148.4286
deaths 2022-02-10 96705 Thursday 203 165.7143
deaths 2022-02-11 96851 Friday 146 186.5714
deaths 2022-02-12 96985 Saturday 134 166.8571